



'''simple docstring'''






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:

     class 				a__:







         @staticmethod
         def      lowercase_			( *__snake_case					:    Tuple		,     **__snake_case					:    int      ):
           pass
def  lowerCamelCase__			(		_A							):
  a		:   Any	  =	hashlib.mda(image.tobytes()							)
  return m.hexdigest()

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				a__(   unittest.TestCase      ):
    lowercase__					=       MODEL_FOR_DEPTH_ESTIMATION_MAPPING







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    List[Any]		,     __snake_case					:    List[str]		,     __snake_case					:    int      ):
      a		:   Dict	  =	DepthEstimationPipeline(model=__snake_case		,     image_processor=__snake_case      )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







    def      lowercase_			( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    int      ):
      a		:   Tuple	  =	depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      self.assertEqual({'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )}		,     __snake_case      )
      import datasets

      a		:   Optional[Any]	  =	datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,     'image'		,     split='test'      )
      a		:   Optional[int]	  =	depth_estimator(
          [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      ),
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              # RGBA
              dataset[0]['file'],
              # LA
              dataset[1]['file'],
              # L
              dataset[2]['file'],
          ]      )
      self.assertEqual(
          [
              {'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},
              {'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},
              {'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},
              {'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},
              {'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},
          ]		,     __snake_case		,     )







    @require_tf
    @unittest.skip('Depth estimation is not implemented in TF'      )
    def      lowercase_			( self					:    Optional[Any]      ):
      pass







    @slow
    @require_torch
    def      lowercase_			( self					:    Any      ):
      a		:   str	  =	'Intel/dpt-large'
      a		:   List[Any]	  =	pipeline('depth-estimation'		,     model=__snake_case      )
      a		:   Tuple	  =	depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'      )
      a		:   Union[str, Any]	  =	hashimage(outputs['depth']      )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()      )		,     29.304      )
      self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()      )		,     2.662      )







    @require_torch
    def      lowercase_			( self					:    Optional[int]      ):
      # This is highly irregular to have no small tests.
      self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'      )




'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )